Application of an autoregressive integrated moving average model in prediction of cardiovascular disease mortality / 上海预防医学

ABSTRACT

Objective:To use autoregressive integrated moving average （ARIMA） model for predicting the mortality of cardiovascular diseases in residents in Yushui District， Jiangxi Province， and to provide basis for developing the prevention and control strategies as well as to promote the continuous optimization of chronic disease prevention and treatment demonstration area. Methods:Based on the cardiovascular death monitoring data of residents in Yushui District， Jiangxi Province from 2014 to 2018， Econometrics View 9.0 software was used to construct the ARIMA seasonal adjustment model to predict the monthly cardiovascular death in this area. Results:The monthly death rate of cardiovascular diseases in Yushui showed a long-term rising trend， with an apparent seasonal pattern （a peak of cardiovascular death from December to January each year）. After the original sequence was subjected to first-order difference and first-order seasonal difference， the difference sequence showed good stationarity （P<0.05）. All the theoretical models were listed and their model parameters were calculated respectively. After statistical test （P<0.05）， 7 alternative models for seasonal adjustment of ARIMA were selected. Among them， ARIMA（1，1，1）（1，1，1）12 is the optimal model selected in this study （R2=0.749， Adjustment R2=0.724， AIC=8.454， SC=8.633， HQ=8.515）.And its residual sequence was tested by white noise test （P>0.05）， indicating that the prediction effect was good. Conclusion:ARIMA（1，1，1）（1，1，1） 12 model can accurately simulate the long-term trend and seasonal pattern of cardiovascular disease death in Yushui， and make a scientific prediction of the trend and monthly distribution of cardiovascular disease death in the next three years.